A Connectivity-Based Psychometric Prediction Framework for Brain–Behavior Relationship Studies. (22nd April 2021)

• Record Type: Journal Article
• Authors: Wu, Jianxiao; Eickhoff, Simon B; Hoffstaedter, Felix; Patil, Kaustubh R; Schwender, Holger; Yeo, B T Thomas; Genon, Sarah
• Is Part Of: Cerebral cortex. Volume 31:Number 8(2021)
• Page Start: 3732
• Page End: 3751
• Publication Date: 2021-04-22
• Subjects: brain–behavior relationships -- behavior prediction -- Human Connectome Project -- machine learning -- resting-state functional connectivity; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhab044
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain–behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain–behavior provided by these approaches to the brain–behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain–behavior relationships, as well as the unique insight into brain–behavior relationships offered by this approach.